MCGS-SLAM

A Multi-Camera SLAM Framework Using Gaussian Splatting for High-Fidelity Mapping

Anonymous Author

SLAM System Pipeline

Our method performs real-time SLAM by fusing synchronized inputs from a multi-camera rig into a unified 3D Gaussian map. It first selects keyframes and estimates depth and normal maps for each camera, then jointly optimizes poses and depths via multi-camera bundle adjustment and scale-consistent depth alignment. Refined keyframes are fused into a dense Gaussian map using differentiable rasterization, interleaved with densification and pruning. An optional offline stage further refines camera trajectories and map quality. The system supports RGB inputs, enabling accurate tracking and photorealistic reconstruction.

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21629mp4

While the World Cup clip is the most significant, other files with this generic naming convention include:

After Spain defeated the Netherlands to win their first-ever World Cup in Johannesburg, South Africa, team captain Iker Casillas was interviewed by his then-girlfriend, Sara Carbonero. Overwhelmed with emotion during the live broadcast, Casillas cut the interview short by kissing Carbonero, a moment that became an iconic symbol of Spain's victory.

Hosted on platforms like Vimeo for specific image-sa archival collections.

A video on Facebook demonstrating specialized techniques like "kettling".


Analysis of Single-Camera and Multi-Camera SLAM (Mapping)

While the World Cup clip is the most significant, other files with this generic naming convention include:

After Spain defeated the Netherlands to win their first-ever World Cup in Johannesburg, South Africa, team captain Iker Casillas was interviewed by his then-girlfriend, Sara Carbonero. Overwhelmed with emotion during the live broadcast, Casillas cut the interview short by kissing Carbonero, a moment that became an iconic symbol of Spain's victory.

Hosted on platforms like Vimeo for specific image-sa archival collections.

A video on Facebook demonstrating specialized techniques like "kettling".


Analysis of Single-Camera and Multi-Camera SLAM (Tracking)

In this section, we benchmark tracking accuracy across eight driving sequences from the Waymo dataset (Real World). MCGS-SLAM achieves the lowest average ATE, significantly outperforming single-camera methods.
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We further evaluate tracking on four sequences from the Oxford Spires dataset (Real World). MCGS-SLAM consistently yields the best performance, demonstrating robust trajectory estimation in large-scale outdoor environments.
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